Drug Sites Prediction and Computational Drug Screening for SARS‐CoV‐2 Proteins

Abstract

The COVID‐19 pandemic, caused by the Severe Acute Respiratory Coronavirus 2 (SARS‐CoV‐2) virus, first started in the Wuhan region of Hubei, China, and has quickly spread to 191 countries and territories, infecting more than 86.4 million people, and resulting in 1.87 global deaths as of January 6th. With SARS‐CoV‐2’s genomic sequence and protein structures deciphered and updated rapidly, clinical treatments and vaccine developments have proceeded simultaneously as researchers attempt to learn more about the infecting mechanisms of this virus. Among these attempts, computational drug screening for SARS‐CoV‐2 has potential for: (1) narrowing down billions of chemical compounds into a list of possible high‐affinity ligands for SARS‐CoV‐2 protein targets, (2) providing information about the activities of SARS‐CoV‐2 proteins, (3) offering possible treatments, and (4) assisting in scientific knowledge to fight against future coronavirus infections. In this work, computational ligand screening for SARS‐CoV‐2 is a combination of site prediction using machine learning technology Partial Order Optimum Likelihood (POOL) and molecular docking. Among the techniques deployed, the machine learning technology POOL was developed by us and assists in the drug screening process for SARS‐CoV‐2 by predicting targeted protein sites, including those that are not the obvious catalytic sites, such as exosites, allosteric sites, and other interaction sites. Results will be presented for the SARS‐CoV‐2 main protease, non‐structural protein 1 (Nsp1), non‐structural protein 9 (Nsp9), and non‐structural protein 15 (Nsp15). Compounds are taken from a variety of libraries, including the ZINC and Enamine databases. Protein structures are downloaded from the Protein Data Bank (http://www.rcsb.org). Molecular dynamic structure simulations are used to generate structures for ensemble docking.